CN1504793A - 3D Random Screens for LCD Displays and Video - Google Patents

Abstract

The patch matrix generator defines a three-dimensional array of pixels called a "cluster array", designating each pixel as either a "cluster pixel" or a "blank pixel" depending on its value. The patch matrix generator also defines a three-dimensional array called a "patch matrix" and assigns grading values to the patch matrix in two stages. In the first stage, the patch matrix generator identifies the largest cluster in the cluster array, identifies cluster pixels located in the largest cluster, assigns graded values to elements in the patch matrix corresponding to the cluster pixels, and eliminates the cluster pixels from the cluster array. Repeat this process until the cluster array contains no cluster pixels. In the second stage, the color patch matrix generator resets the pixels in the cluster array to their initial values, identifies the largest blank in the cluster array, identifies the blank pixels in the largest blank, and assigns the graded value to the element in the color patch matrix corresponding to the blank pixel and Insert cluster pixels in place of blank pixels. Repeat this process until the cluster array contains only cluster pixels.

Description

3D Random Screens for LCD Displays and Video

technical field

The present invention relates to the processing of image data for enhancing the appearance of three-dimensional image data provided in video form or by devices such as liquid crystal displays ("LCDs").

Background technique

Many devices designed to generate color images from rasterized image data cannot take full advantage of the color information provided in the image data. Some devices have technical limitations that prevent them from producing the full range of colors represented in the image data. For example, in some rasterized image data, the color information of each pixel includes data for three constituent colors such as red, green, and blue, the brightness of each of these three colors is represented by eight bits, and for each The component colors allow for 256 possible brightness levels. If an image represented in this way is displayed on a device capable of producing only eight different brightness levels for each component color, a great deal of color information is lost. In these cases, the color information is usually quantized using a quantization method, such as uniform quantization, allowing the device to display the image using fewer colors than provided in the image data. In addition to removing color information from the image, quantization methods often lead to visible artifacts in the output image. For example, if the quantization of image data causes visible transitions between color levels, an effect known as contour emphasis may occur.

The need to optimize the color capabilities of display devices arises not only in the display of two-dimensional ("2-D") image data, but also in the display of three-dimensional ("3-D") image data, such as video. As used herein, the term "three-dimensional image data" refers to digital image data in which the color of a pixel is not only a function of two spatial coordinates, but also a function of a temporal coordinate. When three-dimensional image data is presented on a display device such as an LCD or computer monitor, individual pixels on the screen may display different colors over time. Arrays of pixels with the same time coordinates are generally referred to as "frames". Multiple frames are typically displayed or "cycled" in rapid succession, resulting in a moving image.

One technique used to expand the range of colors achievable by devices with limited color capabilities is called 3D meshing. In general, mesh technology exploits the properties of the human eye to expand the color output of a device beyond its native color capabilities. If a graph composed of sufficiently small dots with different colors is viewed with the naked eye, the viewer generally does not perceive the color of each point, but a color approximately equal to the average color of each point in the graph. The human eye's ability to perceive an average color operates spatially when the eye views a small array of dots on a display device, and over time when the eye views an array of pixels whose color changes rapidly. In either case, the eye typically perceives a color that is approximately equal to the average color of the pixel being viewed. The color perceived by the viewer is called "effective color output". Screening techniques for producing moving images typically construct a three-dimensional array of values, or "screen," which can be used to produce a color pattern within an array of pixels with the desired effective color output. These techniques enable a device to expand the number of colors it can display by creating additional color levels that appear to have intermediate color levels defined by the physical characteristics of the device.

A common three-dimensional grid technique known as frame rate modulation ("FRM") alternates pixels on and off over multiple frames, producing an effective color output approximately equal to the average color measured over a series of frames. The term "refresh rate" refers to how often the output of a single pixel changes between different color levels. Increasing the refresh rate will allow the device to display a greater number of intermediate colors and may provide greater control over effective color output.

Another factor that affects the perceived quality of color output is the perceived uniformity of the color graphics. Screens that produce color graphics with a high degree of uniformity are preferably used in rendering three-dimensional image data. Known FRM techniques have been inconsistent in their ability to produce uniform patterns.

There is a need to develop an improved 3D grid technique that increases the number of color gradations that a device can produce when rendering 3D image data and that provides greater control over the uniformity of the displayed color graphics while reducing the occurrence of artifacts. to the least.

Contents of the invention

It is an object of the present invention to enable a display device to generate multiple color gradations for rendering three-dimensional image data by generating three-dimensional screens from arbitrary three-dimensional color graphics.

An initial pattern generator defines a three-dimensional array of pixels called an "initial pattern." Using an initial pattern as a starting point, the Uniform Pattern Generator defines a three-dimensional array of pixels called a "cluster array", applies filters to identify the largest cluster in the cluster array, eliminates cluster pixels that are in the largest cluster, identifies the largest cluster in the cluster array whitespace, and place cluster pixels in the largest whitespace. The uniform pattern generator continuously moves clustered pixels from the largest cluster to the largest blank until the step of eliminating clustered pixels from the largest cluster creates the largest blank. When this occurs, the process is complete and the modified cluster array is called a uniform graph. The dither matrix generator defines a three-dimensional array called a "dither matrix" and assigns grading values to the dither matrix in two stages. In the first stage, the patch matrix generator identifies the largest cluster in the uniform pattern, identifies the cluster pixels that lie in the largest cluster, and assigns a graded value to the element in the patch matrix that corresponds to the cluster pixel. Clustered pixels are then eliminated from the uniform pattern. Repeat this process until the homogeneous graph does not contain any clustered pixels. In the second stage, the patch matrix generator resets the pixels in the uniform pattern to their initial values, identifies the largest void in the uniform pattern, identifies empty pixels in the largest void, and inserts cluster pixels at the positions of the empty pixels. The patch matrix generator assigns grading values to elements in the patch matrix that correspond to blank pixels. This process is repeated until the homogeneous pattern contains only clustered pixels.

Description of drawings

Figure 1 illustrates the main components in a typical image reproduction system.

Figure 2 illustrates the major components in a typical personal computer system.

Figure 3 is a block diagram of a system that may be used to implement aspects of the invention.

Figures 4(a)-4(c) illustrate three frames of random patterns that can be used as initial patterns.

Figure 4(d) illustrates the image produced by averaging the pixel values in the frames shown in 4(a)-4(c).

Figure 5 illustrates a flowchart summarizing the steps performed by the uniform pattern generator and the patch matrix generator.

Figure 6(a) schematically illustrates a two-dimensional cluster array of binary values.

Figure 6(b) schematically illustrates a filter that can be applied to the array of clusters shown in Figure 6(a) to identify the largest clusters or largest blanks.

Figure 6(c) schematically illustrates the cluster array of Figure 6(a) replicated four times.

Figure 6(d) numerically illustrates the details of applying the filter shown in Figure 6(b) to the region of the cluster array of Figure 6(a).

Figure 6(e) schematically illustrates an array of total density values for pixels in the cluster array shown in Figure 6(a).

Figures 7(a)-7(c) illustrate three frames of a uniform pattern generated from the initial pattern shown in Figures 4(a)-4(c).

Figure 7(d) illustrates the image produced by averaging the values of the pixels in the frames shown in Figures 7(a)-7(c).

Figures 8(a) and 8(b) illustrate example values for the Gaussian filter.

Figures 9(a) and 9(b) illustrate example values for a filter.

Figures 10(a)-10(c) illustrate three frames of a uniform pattern generated from the initial pattern shown in Figures 4(a)-4(c).

Figure 10(d) illustrates the image produced by averaging the values of the pixels in the frames shown in Figures 10(a)-10(c).

Figures 11(a)-11(c) illustrate three frames of a uniform pattern generated from the initial pattern shown in Figures 4(a)-4(c).

Figure 11(d) illustrates the image produced by averaging the values of the pixels in the frames shown in Figures 11(a)-11(c).

Figures 12(a)-12(c) illustrate three frames of a uniform pattern generated from the initial pattern shown in Figures 4(a)-4(c).

Figure 12(d) illustrates the image produced by averaging the values of the pixels in the frames shown in Figures 12(a)-12(c).

Detailed ways

System Overview

Figure 1 illustrates the main components in a typical image reproduction system. Input device 10 receives a signal representing an original image from path 1 and generates a rasterized representation of the original image along path 11 . Control means 20 receives this representation from path 11 and in response generates along path 21 an output device-dependent representation of the original image. Output device 30 receives this representation from path 21 and in response generates along path 31 a representation of the original image. The invention aims at improving the perceived quality of the representations produced by the output device.

The input device 10 may be a software application capable of generating three-dimensional graphical images. Alternatively, the input device 10 may also be a device capable of generating three-dimensional photographic images, such as a video camera. If the input device 10 is a software application that creates an image, the signal received from path 1 may represent commands or data for the application.

Output device 30 may be any type of device capable of rendering video or three-dimensional graphic images. For example, output device 30 may be an LCD device, or may also be a computer monitor.

The control means 20 are responsible for converting the rasterized representation of the original image received from the pathway 11 into an output device dependent representation of the original image. The control device 20 can be implemented by software and/or hardware in a general-purpose computer, as shown in FIG. 2 .

Figure 2 illustrates the major components in a typical personal computer system that may be used to implement aspects of the present invention. CPU 42 provides computing resources. I/O control 43 represents an interface with an I/O device 44 such as a keyboard, mouse or modem. RAM 45 is a system random access memory. The storage control 46 represents an interface with a storage device 47 comprising a storage medium such as a magnetic tape or a magnetic disk or an optical medium. The storage medium may be used to record programs of instructions used by operating systems, utilities, and application programs, and may contain programs that implement aspects of the present invention. Display control 48 provides an interface with display device 49 . The display device may be any type of visual display device. Control 50 represents an interface with an input device 10 such as a software application or a video camera capable of generating graphical images. Control 52 represents an interface with output device 30, such as an LCD device.

In Figure 2, all major system components are connected to a bus 41 which may represent more than one physical bus. No bus architecture is required to implement the invention.

The functionality of one or more computer components and aspects of the invention may be implemented by a wide variety of circuitry including discrete logic components, one or more ASICs, and/or software controlled processors. For example, the control device 20 can be realized by a dedicated device. The manner in which the control device 20 is implemented is not critical to the invention. Other implementations including digital and analog processing circuits may be used. Information may be conveyed via a variety of machine-readable media such as baseband or modulated communication paths over a spectrum covering frequencies from ultrasonic to ultraviolet frequencies, or including storage using essentially any magnetic or optical recording technique, including magnetic tape, magnetic disk, and optical disk media etc. to deliver the software implementation of the present invention.

Figure 3 is a block diagram of a system that may be used to implement aspects of the invention. Initial pattern generator 510 generates data representing a pattern of values and transmits the data along path 515 to uniform pattern generator 520 . Uniform pattern generator 520 processes the data received along path 515 and transmits the data along path 525 to patch matrix generator 540 . Patch matrix generator 540 uses the data received along path 525 to generate a three-dimensional screen and sends data representing the screen to storage device 47 along path 545 . Halftone processor 729 receives the rasterized three-dimensional image data along path 740 , accesses data representing the screen in storage device 47 , and transmits the halftone representation of the image to output device 30 along path 744 . Output device 30 produces an output image along path 753 . In a preferred implementation, initial pattern generator 510 , uniform pattern generator 520 , and patch matrix generator 540 are implemented by one or more software applications incorporated into control device 20 . Another solution is that the initial pattern generator 510 , the uniform pattern generator 520 and the color block matrix generator 540 are implemented by digital or analog circuits integrated in the control device 20 . In another implementation, the initial pattern generator 510, the uniform pattern generator 520, and the patch matrix generator 540 are integrated in the output device 30 and are implemented by one or more software executable by the output device 30 or by digital or analog circuits. application to achieve. In another implementation, the initial pattern generator 510 , the uniform pattern generator 520 and the patch matrix generator 540 are integrated in the control device 20 , and the storage device 47 and the halftone processor 729 are integrated in the output device 30 .

initial pattern generator

The initial pattern generator 510 defines a three-dimensional array of pixels of dimension MxNxT, where M and N represent the two spatial dimensions, and T represents the temporal dimension. The three-dimensional array of pixels is called the "prime pattern". The pixel values in the original graphics can be generated in any way. In a preferred implementation, each pixel in the original pattern has a binary value. In this implementation, pixels with a binary value of 1 are called "cluster pixels" and pixels with a binary value of 0 are called "blank pixels". 4(a)-4(c) illustrate a first frame 422, a second frame 423, and a third frame 424 of a random pattern of dimension 128x128x3 that can be used as an initial pattern. Figure 4(d) illustrates the image produced by averaging the pixel values in frames 422, 423, and 424, and represents the image as seen by a viewer when frames 422, 423, 424 are presented in a rapid succession. In another implementation, the pixels in the original pattern have non-binary values, where cluster pixels and blank pixels are defined as pixels with values within a certain range.

uniform pattern generator

FIG. 5 illustrates a flowchart summarizing the steps performed by the uniform pattern generator 520 and the patch matrix generator 540 . Steps 550-557 are performed by the uniform pattern generator 520. In a preferred implementation, uniform pattern generator 520 defines an MxNxT array, referred to as a "cluster array". In step 550, the uniform pattern generator 520 copies the initial pattern to the cluster array by assigning to each pixel in the cluster array the value of the corresponding pixel in the original pattern.

Identification of largest clusters

The uniform pattern generator 520 converts the cluster array into a uniformly distributed pattern as follows. At step 551, the uniform pattern generator 520 identifies the largest cluster in the array of clusters. In the best implementation, the largest clusters are identified by a filter. If an XxYxZ filter is employed, the XxYxZ filter window is dragged pixel by pixel through the three-dimensional cluster array to examine each XxYxZ set or region of adjacent pixels in the cluster array. Uniform pattern generator 520 applies a filter to a region, producing output values by convolving pixel values in the region with the filter function. Pixels in this area are selected and designated as the "central pixel" of this area. The selection of the central pixel of the region can be arbitrary, or can also be determined by filter characteristics or other factors. The resulting output value from the corresponding region is called the "total density value" and is related to the central pixel of the region. Figure 6(a) schematically illustrates a two-dimensional 6x6 cluster array 705 of binary values. A part of pixels including pixel 723 has a value of "0". Other pixels, including pixel 724, have a "1" value. Figure 6(b) schematically illustrates a 3x3 filter that can be applied to the cluster array 705 shown in Figure 6(a) to identify the largest clusters. Although a two-dimensional cluster array 705 and a two-dimensional filter 710 are shown in FIGS. Proceed in the same way. The cluster array is replicated as many times as necessary to allow the filter to wrap around to produce the total density value of the boundary pixels. FIG. 6( c ) schematically illustrates cluster array 705 replicated four times to illustrate the application of filter 710 to a region 727 of nine pixels centered on pixel 723 . Filter 710 convolves with the values in region 727 , yielding an overall density value for central pixel 723 . FIG. 6( d ) numerically illustrates the application of filter 710 to region 727 . Referring to the numerical calculations shown in Figure 6(d), region 727 has an overall density value of 2.2. Filter 710 is applied to each 3×3 region in cluster array 705 . FIG. 6( e ) schematically illustrates an array 733 of total density values of the cluster array 705 . Each value in array 733 corresponds to a corresponding pixel in cluster array 705 and represents the total density value for the area centered on the corresponding pixel. For example, element 736 corresponds to pixel 723 and contains a value of 2.2, which represents the total density value associated with region 727 . Element 738 corresponds to pixel 724 and contains a value of 4.5, which represents the total density value for the area centered on pixel 724 .

The pixel in the cluster array with the largest total density value is identified, then the surrounding area is determined to be the largest cluster. Referring to FIG. 6( e ), 4.5 is the maximum total density value in array 733 , indicating that the region centered on the corresponding pixel 724 is the largest cluster in cluster array 705 .

In step 552, the uniform pattern generator 520 eliminates the cluster pixels located in the center of the largest cluster. In the best implementation, the elimination of cluster pixels is achieved by identifying the cluster pixel at the center of the largest cluster and changing its value from 1 to 0. If the central pixel in the largest cluster is a blank pixel, the cluster pixel is eliminated from the second largest cluster with a cluster pixel in the center. In another implementation, non-central pixels in the largest cluster may be eliminated.

Identification of the largest blank

At step 553, uniform pattern generator 520 identifies the largest void in the cluster array. In an optimal implementation, the filter used to identify the largest clusters is also used to identify the largest gaps. The total density value of each region in the cluster array is checked, and the region with the smallest total density value is identified as the largest blank.

At step 555, the cluster pixels are placed in the largest margin. In the best implementation, cluster pixels are placed in the largest margin by identifying the margin pixel that is in the center of the largest margin and changing its value from 0 to 1. If the central pixel of the largest blank is a cluster pixel, then the cluster pixel is placed in the second largest blank with a blank pixel in the center. In another implementation, pixels that are not centrally located in the largest margin can be eliminated.

If step 554 determines that the largest void is the region from which the cluster pixel was just eliminated, then step 556 restores the cluster pixel to its position in the center of the region, completing the process of forming the cluster array. Otherwise, the uniform pattern generator 520 returns to step 551 and examines the modified array of clusters, identifies the largest clusters, and repeats the process. Uniform pattern generator 520 continues to move cluster pixels from the largest cluster into the largest void until the step of eliminating cluster pixels from the largest cluster creates the largest void. When this occurs, the method proceeds to step 556 as described above to complete the process. The modified cluster array is called a uniform graph.

7(a)-7(c) illustrate a first frame 782, a second frame 783, and a third frame 784 of the uniform pattern generated from the initial pattern shown in Figs. 4(a)-4(c). FIG. 7( d ) illustrates the image produced by averaging the values in frames 782 , 783 and 784 . At step 557, the uniform pattern is stored in memory as needed.

Color Patch Matrix Generator

Steps 560 - 576 are performed by the patch matrix generator 540 . In a preferred implementation, patch matrix generator 540 receives data from path 525 representing a uniform pattern. In this implementation, the patch matrix generator 540 defines an MxNxT cluster array, which serves the same function as the cluster array used by the uniform pattern generator 520 . In step 560, patch matrix generator 540 copies the uniform pattern into the cluster array by assigning to each pixel in the cluster array the value of the corresponding pixel in the uniform pattern. It has been found that using a uniform pattern as the starting point for the patch matrix generator 540 is extremely advantageous due to its uniform distribution. In other implementations, different graphics can be loaded into the cluster array in the patch matrix generator 540 . In step 561, patch matrix generator 540 establishes variable RANK, which indicates the number of cluster pixels currently present in the cluster array. For example, in an optimal implementation, if the uniform graph contains Q clustered pixels, assign RANK an initial value equal to Q.

Patch matrix generator 540 defines a second MxNxT array, referred to as "patch matrix", containing MxNxT elements. Since the color block matrix and the cluster array have the same dimension, there is a corresponding relationship between the elements in the color block matrix with coordinates (i, j) and the pixels in the cluster array with coordinates (i, j).

Assign values to elements in the patch matrix in two stages. In the first stage, according to steps 563 and 564, the patch matrix generator 540 identifies the largest cluster in the array of clusters, identifies the pixel located in that cluster, and assigns a ranking value equal to the variable RANK to the patch corresponding to that pixel elements in the matrix. At step 565, cluster pixels are eliminated from future consideration. In the best implementation, the identification and elimination of cluster pixels is achieved by identifying the central pixel of the largest cluster and changing its value from 1 to 0. If the central pixel of the largest cluster is a blank pixel, the central pixel of the second largest cluster is identified and eliminated. In step 566, the variable RANK is decremented by 1, reflecting the elimination of a cluster pixel from the cluster array. This process is repeated until the cluster array does not contain any cluster pixels, as determined in step 562 . This process produces Q unique grading values (from Q down to 1) assigned to selected elements in the patch matrix.

The second stage assigns grading values from Q+1 up to MxNxT to the elements in the patch matrix. The second stage begins at step 570, where the patch matrix generator 540 again copies the uniform pattern into the cluster array. In step 571, the value of RANK is reset to Q, reflecting the number of cluster pixels in the cluster array. In steps 573 and 574, the patch matrix generator 540 identifies the largest blank in the cluster array, identifies blank pixels in the largest blank, and inserts cluster pixels at the location of the blank pixels. In the best implementation, cluster pixels are interpolated by identifying the central pixel in the largest void and changing its value from 0 to 1. In steps 575 and 576, the patch matrix generator 540 increments the value of RANK by 1, reflecting the addition of a cluster pixel to the cluster array and assigning a grading value equal to the adjusted value of RANK for the element in the patch matrix corresponding to the central pixel . This process is repeated until the cluster array contains only cluster pixels, as determined in step 572 . The final result of the two-stage process is a matrix of patches containing M×N×T unique values.

Normalized

The above process produces an MxNxT patch matrix containing MxNxT unique grading values. If L grading values are desired instead of M x N x T grading values, each grading value R can be converted to a normalized grading value R' by applying the following formula:

${\mathrm{<span\; class="notranslate">\; R</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; int</span>}<span\; class="notranslate">\; \{</span>\frac{\mathrm{<span\; class="notranslate">\; R</span>}}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In the above formula, int{x} produces an integer value by rounding, ie by dropping all digits to the right of the decimal point in the variable x.

filter selection

The choice of filter affects the performance and appearance of the 3D image and is therefore an essential element of 3D screen implementation. One implementation uses a Gaussian filter defined as follows:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Figures 8(a) and 8(b) illustrate example values for the Gaussian filter defined above with a 5x5x3 filter window representing 5x5 pixels in two spatial dimensions and 3 frames in the temporal dimension. Figure 8(a) illustrates the value of f(x,y,t) at t=0. Figure 8(b) illustrates the value of f(x,y,t) for t=±1. Referring to Figures 7(a)-7(c), by applying the Gaussian filter defined by the above formula (2) to the initial graphics shown in Figures 4(a)-4(c), frames 782, 783 and 784 are generated uniform graphics.

Since the Gaussian filter treats spatial and temporal information equally, it does not adapt well to the human visual system, which processes spatial and temporal information differently. Another filter for this difference is defined by:

in

and α is a real number.

The e ^{-(x2+y2/2σ2)} term affects the spatial uniformity, $\frac{\mathrm{\&alpha;}}{1+t^2}\mathrm{\&delta;}(x,\mathrm{the\; y})$ Item affects graph performance in the time dimension. Figures 9(a) and 9(b) illustrate example values for the filters defined by equations (3) and (4) with a 5x5x3 filter window of σ = 1.0. Figure 9(a) illustrates the value of f(x,y,t) at t=0. Figure 9(b) illustrates the value of f(x,y,t) for t=±1. In Fig. 9(b), the value of α is not specified. The filters shown in Figures 9(a) and 9(b) produce different visual patterns depending on the value of α. Increasing the alpha value generally produces graphics with a more uniform appearance. This result is extremely useful at high refresh rates. It has been observed that among various graphs showing the same average brightness over a given number of frames, those graphs showing higher uniformity are more visually pleasing. Figures 10(a)-10(c) illustrate the uniform Three frames 906, 907 and 908 of graphics. FIG. 10( d ) illustrates an image generated by looping frames 906 , 907 and 908 .

If α = 0.0, the filter function provides no weighting for previous or subsequent frames, but computes output values for each frame separately. Figures 11(a)-11(c) illustrate three frames 911, 912, and 913 of the uniform pattern generated from the initial pattern shown in Figures 4(a)-4(c) when a = 0.0. FIG. 11( d ) illustrates an image generated by looping frames 906 , 907 , and 908 .

If α < 0, the filter function typically produces frames with little or no difference. This result is extremely useful when slowly cycling through these frames, since visual differences between frames can produce unwanted "flicker" at low refresh rates. Figures 12(a)-12(c) illustrate three frames 916, 917, and 918 of the uniform pattern generated from the initial pattern shown in Figures 4(a)-4(c) when a = -0.6. The dot pattern is the same in all three frames. FIG. 12( d ) illustrates an image generated by looping frames 906 , 907 and 908 .

Claims (12)

1. A method for generating a three-dimensional color patch matrix for representing one or more color levels in an image, wherein the method comprises: receiving data representing a three-dimensional array of pixels, wherein each pixel has a value, and assigning each pixel to a first set comprising cluster pixels arranged in clusters or to a blank pixel comprising arranged in spaces based on the value of each pixel the second set of defining a three-dimensional patch matrix comprising a plurality of elements, wherein each pixel in the array has a corresponding element in the patch matrix; by identifying cluster pixels that are in the largest cluster within said array, updating a ranking value indicating the order in which said cluster pixels were identified, generating a value for said corresponding element in said patch matrix based on said ranking value, and from said Eliminating the cluster pixels from the first set, and determining the value of each element in the color patch matrix corresponding to the cluster pixels; and By identifying the blank pixel that is in the largest blank within the array, updating the ranking value indicating the order in which the blank pixel was identified, generating the value of the corresponding element in the patch matrix based on the ranking value, and from the second Eliminate the blank pixels in the set, and determine the value of each element in the color block matrix corresponding to the blank pixels. 2. The method according to claim 1, wherein the initial value of the pixel in the array is determined by the following operations: receiving data representing a three-dimensional initial pattern of pixels, each pixel having a value, and assigning each pixel to a first set comprising cluster pixels arranged in clusters or to a blank comprising clusters arranged in blanks according to the value of each pixel the second set of pixels; and by iteratively identifying cluster pixels within the largest cluster within the initial pattern, reallocating the cluster pixels from the first set to the second set, identifying blank pixels within the largest void within the initial pattern, and reallocating the blank pixels from the second set to the first set, adjusting the values of pixels in the initial pattern. 3. The method of claim 1, wherein each pixel in the array has a binary value. 4. The method of claim 1, wherein the step of updating the binning value is performed by subtracting the binning value by one for each cluster pixel eliminated from the first set and for each cluster pixel eliminated from the second set. This is done by adding one to the grading value for each blank pixel eliminated in the two sets. 5. An apparatus comprising a memory and processing circuitry coupled to said memory, wherein said processing circuitry is adapted to: receiving data representing a three-dimensional array of pixels, wherein each pixel has a value, and assigning each pixel to a first set comprising cluster pixels arranged in clusters or to a blank pixel comprising arranged in spaces based on the value of each pixel the second set of defining a three-dimensional patch matrix comprising a plurality of elements, wherein each pixel in the array has a corresponding element in the patch matrix; by identifying cluster pixels that are in the largest cluster within said array, updating a ranking value indicating the order in which said cluster pixels were identified, generating a value for said corresponding element in said patch matrix based on said ranking value, and from said Eliminating the cluster pixels from the first set, and determining the value of each element in the color patch matrix corresponding to the cluster pixels; and By identifying the blank pixel that is in the largest blank within the array, updating the ranking value indicating the order in which the blank pixel was identified, generating the value of the corresponding element in the patch matrix based on the ranking value, and from the second Eliminate the blank pixels in the set, and determine the value of each element in the color block matrix corresponding to the blank pixels. 6. The device according to claim 5, wherein the initial value of the pixel in the array is determined by the following operations: receiving data representing a three-dimensional initial pattern of pixels, each pixel having a value, and assigning each pixel to a first set comprising cluster pixels arranged in clusters or to a blank comprising clusters arranged in blanks according to the value of each pixel the second set of pixels; and by iteratively identifying cluster pixels in the largest cluster within the initial pattern, reallocating the cluster pixels from the first set to the second set, identifying blank pixels in the largest void in the initial pattern, and reallocating the blank pixels from the second set to the first set, adjusting the values of pixels in the initial pattern. 7. The apparatus of claim 5, wherein each pixel in the array has a binary value. 8. The apparatus of claim 5, wherein the step of updating the grading value is performed by subtracting the grading value by one for each cluster pixel eliminated from the first set and for each cluster pixel eliminated from the second set. This is done by adding one to the grading value for each blank pixel eliminated in the two sets. 9. A device-readable medium containing a program of instructions executed by said device to implement a method for generating a three-dimensional matrix of color patches for representing one or more color levels in an image, wherein said Methods include: receiving data representing a three-dimensional array of pixels, wherein each pixel has a value, and assigning each pixel to a first set comprising cluster pixels arranged in clusters or to a blank pixel comprising arranged in spaces based on the value of each pixel the second set of defining a three-dimensional patch matrix comprising a plurality of elements, wherein each pixel in the array has a corresponding element in the patch matrix; by identifying cluster pixels that are in the largest cluster within said array, updating a ranking value indicating the order in which said cluster pixels were identified, generating a value for said corresponding element in said patch matrix based on said ranking value, and from said Eliminating the cluster pixels from the first set, and determining the value of each element in the color patch matrix corresponding to the cluster pixels; and By identifying a blank pixel located in the largest blank within the array, updating a ranking value indicating the order in which blank pixels were identified, generating a value for the corresponding element in the patch matrix based on the ranking value, and from the second Eliminate the blank pixels in the set, and determine the value of each element in the color block matrix corresponding to the blank pixels. 10. The medium of claim 9, wherein initial values of pixels in the array are determined by: receiving data representing a three-dimensional initial pattern of pixels, each pixel having a value, and assigning each pixel to a first set comprising cluster pixels arranged in clusters or to a blank comprising clusters arranged in blanks according to the value of each pixel the second set of pixels; and by iteratively identifying cluster pixels in the largest cluster within the initial pattern, reallocating the cluster pixels from the first set to the second set, identifying blank pixels in the largest void in the initial pattern, and reallocating the blank pixels from the second set to the first set, adjusting the values of pixels in the initial pattern. 11. The medium of claim 9, wherein each pixel in the array has a binary value. 12. The medium of claim 9 , wherein the step of updating the grading value is performed by subtracting the grading value by one for each cluster pixel eliminated from the first set and for each cluster pixel eliminated from the first set. This is done by adding one to the grading value for each blank pixel eliminated in the two sets.

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